Intel Corporation’s Post

Unlock the full potential of your Large Language Models (LLMs) with Intel® Extension for PyTorch (IPEX) and the Intel® LLM Library for PyTorch (IPEX-LLM). Download this whitepaper to explore the optimization of LLMs, including their performance, resource utilization, and response times in real-world applications. Link - https://intel.ly/3BBTlHG

Unlock the full potential of your Large Language Models (LLMs) with Intel® Extension for PyTorch (IPEX) and the Intel® LLM Library for PyTorch (IPEX-LLM). Download this whitepaper to explore the optimization of LLMs, including their performance, resource utilization, and response times in real-world applications. Link - https://intel.ly/3Y6YsIO

Unlock the full potential of your Large Language Models (LLMs) with Intel® Extension for PyTorch (IPEX) and the Intel® LLM Library for PyTorch (IPEX-LLM). Download this whitepaper to explore the optimization of LLMs, including their performance, resource utilization, and response times in real-world applications. Link - https://intel.ly/4dwlqgQ

Unlock the full potential of your Large Language Models (LLMs) with Intel® Extension for PyTorch (IPEX) and the Intel® LLM Library for PyTorch (IPEX-LLM). Download this whitepaper to explore the optimization of LLMs, including their performance, resource utilization, and response times in real-world applications. Link - https://intel.ly/4gMoCYK

With Intel, CPUs are able to process LLMs, this is all thanks to Intel® Extension for PyTorch (IPEX) and the Intel® LLM Library for PyTorch (IPEX-LLM). Find out more by downloading this whitepaper in the link attached. #IamIntel #IntelXeon Erica Chen, DBA Ir. Dr. Seong Boon Ngoo

Unlock the full potential of your Large Language Models (LLMs) with Intel® Extension for PyTorch (IPEX) and the Intel® LLM Library for PyTorch (IPEX-LLM). Download this whitepaper to explore the optimization of LLMs, including their performance, resource utilization, and response times in real-world applications. Link - https://intel.ly/3Y7RSBO

"Using these optimizations, you can enjoy up to three times the out-of-the-box acceleration, depending on batch size and input sequence length." Great blog introducing several software optimization techniques to deploy state-of-the-art #LLMs on #AMD #CDNA2 #GPUs. "These include PyTorch 2 compilation, Flash Attention v2, paged_attention, PyTorch TunableOp, and multi-GPU inference"

Imagine having a language model powerful like GPT 3.5 running on your local machine 😎 Thanks to Ollama and Phi3 for this gift, it is powerful, runs on CPU, is fast, and is OS Independent. #LLM #opensource #ollama #SLM #deeplearning

Model Quantization in TFLite for Edge Inference. ➡ TensorFlow Lite Provides a mobile-optimized inference engine for TensorFlow models. ➡ Quantization brings improvements via model compression and latency reduction. With the API defaults, the model size shrinks by 4x, and we typically see between 1.5 - 4x improvements in CPU latency. The model we are exploring today is a computer vision model that recognizes hand gestures for the rock, paper, scissors game! It was about 100% accurate in training/validation and about 80% accurate in test after quantization #TinyML #ML #Quantization